Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/48636
Title: Detecting the Violation of Homogeneity in Mixed Models : A Case Study
Authors: FANG XICHENG
Keywords: Generalized linear model, random effects, outlier, homogeneity assumption, correlated
Issue Date: 20-Aug-2013
Source: FANG XICHENG (2013-08-20). Detecting the Violation of Homogeneity in Mixed Models : A Case Study. ScholarBank@NUS Repository.
Abstract: There has been no systematic approach for checking the homogeneity assumption for generalized linear mixed-effects models. Extreme outliers that behave differently from the population may cause problems for model fitting and interpretation. We propose two tests based on random effects where the covariance matrices may be computed from the fitted model covariance parameters or the empirical variation of random effects. The tests may serve as a tool to detect outliers that violate homogeneity in mixed-effects models. Extensive simulations are carried out to assess the performance of our methods. A real case study of arthritis disease is included to provide further illustration. The results suggest removing outliers may change the signs and magnitude of important predictors in the model.
URI: http://scholarbank.nus.edu.sg/handle/10635/48636
Appears in Collections:Ph.D Theses (Open)

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